Due to significant differences betw een the Polish regions, follow ing Boeri and Scarpetta (1995) and B urda and Profit (1996), I estim ate separate regressions for m odem , industrial, and agricultural voivodeships. I follow the division o f voivodeships elaborated by K w iatkow ski and G aw ronska (1995) and K w iatkow ski, Lehm ann and Schaffer (1992). T he division is based on the em ploym ent structure from 1989. All the regions were first divided into two groups: agricultural and non- agricultural voivodeships. The cut-off point was taken as a m ean plus h alf o f standard deviation o f em ploym ent share in agriculture. The voivodeships above this critical value were declared agricultural. The rem aining voivodeships (non-agricultural) w ere subsequently divided into three further groups. Firstly, the authors calculated the m ean share o f industry in non-agricultural em ploym ent. V oivodeships w ere declared strongly industrialised if the corresponding share o f industry was above the m ean m inus half standard deviation. Finally, for the non-agricultural non-strongly-industrial voivodeships, the standard deviation and m ean were calculated for the share o f em ploym ent in the private sector in relation to the overall em ploym ent in non- agricultural sector. V oivodeships with the private sector share at least half o f standard
deviation above the m ean w ere declared m odem . The residual group (non-agricultural,
28
non-strongly-industrialised, non-m odem ) was labelled others.
The geographical distribution o f voivodeships is depicted in the m ap o f Figure 33. Figure 33 Modern, Industrial, and Agricultural Voivodeships
N o tes: T h e striped v o iv o d e sh ip s are m o d em , the black o n e s industrial, the patterned o n e s agricultural, and the w h ite o n e s b e lo n g to n o n e o f th ese ca teg o ries.
Sou rce: K w ia tk o w sk i and G a w ro n sk a (1 9 9 5 ).
28 In ca se o f o ld v o iv o d e sh ip s structure, as agricultural v o iv o d e sh ip s w e c la s s ify B ia ls k o p o d la sk ie , C h elm sk ie, C ie c h a n o w sk ie, K o n in sk ie, K rosn ien sk ie, L e s z c z y n s k ie , L u b e lsk ie, L o m z y n sk ie , N o w o sa d e c k ie , O stro le ck ie, P io trk o w sk ie, P lo c k ie , P rz em y sk ie, R a d o m sk ie , S ie d le c k ie , S iera d zk ie , S k ie m ie w ic k ie , S u w a lsk ie , T a m o b r z e sk ie , T a m o w s k ie , Z a m o jsk ie. A s industrial v o iv o d e s h ip s w e cla ssify : B ielsk ie , B y d g o s k ie , C z e s to c h o w s k ie , Jelen io g rrsk ie, K a lisk ie , K a to w ic k ie , K ie le c k ie , L eg n ick ie, L o d z k ie -m ie jsk ie , O p o lsk ie , R z e s z o w sk ie , T o ru n sk ie, W a lb r z y sk ie, W lo c la w s k ie , W ro cla w sk ie, Z ielo n o g o r sk ie . A n d fin a lly the m o d em v o iv io d h ip s are: W a r s z a w sk ie -sto le c z n e , G d an sk ie, G o rz o w sk ie, K o sz a lin s k ie, K ra k o w sk ie-m iejsk ie, P o zn a n sk ie, S lu p sk ie and S z c z e c in s k ie . B ia lo sto c k ie , E lb lask ie, O ls zty n sk ie and P ilsk ie w ere left o u tsid e th e c la s sific a tio n .
In the n ew adm inistration d iv isio n , for agricultural v o iv o d e sh ip s w e c la s sify : L u b elsk ie, M a lo p o ls k ie, P odkarpackie, P od lask ie, W a rm in sk o -M a zu rsk ie, for industrial v o iv o d e sh ip s: D o ln o s l^ sk ie , K u ja w sk o - P om orsk ie, L o d zk ie, O p o lsk ie , S l^ sk ie, S w i^ to k rzy sk ie, W ie lk o p o ls k ie and for m od ern v o iv o d e sh ip s: L ub u sk ie, M a zo w ie c k ie , P o m o rsk ie, Z a ch o d n io p o m o rsk ie.
The sum m ary o f all coefficients obtained in the estim ations are presented in T able 28. Table 28 Summary of cummulative coefficients obtained from estimations (fixed effects)
Region jtraining & PW Loans
old voivodeships G en eral 0 .0 0 -0.07 -0.02 -0.03 Agricultural -0.01 -0.02 0.0 0 -0.05 Industrial 0.01 -0.11 0 .0 2 -0.05 M odern 0 .0 2 -0.14 0 .1 0 0.00 new voivodeships G e n eral 0 .0 6 0 .0 2 0.01 n/a
g e n e ra l w ithout re c e s s io n period 0 .0 5 -0.07 0 .0 2 n/a
R egression results are presented in Table 29, Table 30 and Table 32. The cum ulative significances for cum ulative coefficients are presented in Table 30, Table 32 and Table 34. These estim ates are m ade for old voivodeships only, due to sm aller sam ples for new voivodeships. The estim ation by type o f voivodeship with the exclusion o f the recession period was not practical due to the small sam ple size. T he H ausm an test rejected the random effects m odel in all the estim ations.
Overall, again the coefficients for unem ploym ent and vacancies are positive in all cases; the figures for unem ploym ent are considerably higher than those for vacancies.
The results for agricultural voivodeships provide the evidence supporting the claim that the training program m es are the least efficient there, with a negative coefficient o f -0.015. The estim ation results for intervention w orks show the strongest substitution/ displacem ent effects for all the types o f voivodeships with the cum ulative coefficient o f -0.023 and overall significance o f 1.47. The effects o f public w orks are the w eakest o f all the types with the cum ulative coefficient o f 0.001 and overall significance o f 4.13. Overall, the A LM Ps in agricultural voivodeships seem to be rather inefficient.
Table 29 Augmented matching function estimates for old agricultural voivodeships.
Fixed Effects Random Effects
outflows Coefficient Std. Error T-statlstics P>|t| Coefficient Std. Error Z-statistics Z>|t|
In unemployment (t-1) 0.431 0.068 6.360 0.000 0.669 0.037 18.080 0.000 In vacancies (t-1) 0 . 0 2 2 0.019 1.130 0.260 0.098 0.013 7.550 0.000 training -0.015 0 . 0 1 1 -1.410 0.158 0.044 0 . 0 1 2 3.580 0.000 In Intervention Works 0.018 0 . 0 2 1 0.850 0.394 0.026 0.026 0.980 0.326 In Intervention Works (t-1) -0.054 0.024 -2.270 0.023 -0.060 0.029 -2.060 0.039 In Intervention Works (t-2) 0.000 0.023 0 . 0 1 0 0.996 0.009 0.029 0.320 0.750 In Intervention Works (t-3) 0.003 0.023 0.140 0.887 0.004 0.029 0.130 0.896 In Intervention Works (t-4) -0.014 0 . 0 2 2 -0.620 0.533 0.000 0.027 0.000 0.997 In Intervention Works (t-5) 0.041 0 . 0 2 2 1.900 0.058 0.032 0.027 1 . 2 2 0 0.224 In Intervention Works (t-6) -0.017 0.019 -0.920 0.357 -0 . 0 0 2 0.023 -0 . 1 0 0 0.920 In Public Works 0.025 0 . 0 1 2 2.080 0.038 0.003 0.014 0.240 0.813 In Public Works (t-1) 0 . 0 2 2 0.013 1.660 0.098 0.008 0.016 0.480 0.633 In Public Works (t-2) -0 . 0 1 2 0.014 -0.860 0.389 -0.013 0.016 -0.850 0.394 In Public Works (t-3) 0 . 0 0 2 0.013 0 . 1 2 0 0.904 -0.005 0.014 -0.340 0.737 In Public Works (t-4) -0.036 0 . 0 1 2 -3.130 0 . 0 0 2 -0.038 0.013 -2.910 0.004 In Startup Loans 0.000 0 . 0 1 2 -0 . 0 1 0 0.990 0.016 0.015 1.040 0.298 In Startup Loans (t-1) -0.004 0.013 -0.310 0.754 0.007 0.017 0.400 0.689 In Startup Loans (t-2) 0.015 0.014 1.070 0.285 0.031 0.019 1.660 0.097 In Startup Loans (t-3) 0.003 0.014 0 . 2 1 0 0.835 0.014 0.017 0.800 0.424 In Startup Loans (t-4) -0.023 0.014 -1.680 0.094 -0.005 0.017 -0.310 0.753 In Startup Loans (t-5) -0.037 0 . 0 1 2 -3.110 0 . 0 0 2 -0.014 0.015 -0.920 0.360 d1 0.000 0.000 4.670 0.000 0.000 0.000 4.680 0.000 d2 0.000 0.000 3.930 0.000 0.000 0 . 0 0 0 2.610 0.009 d3 0.000 0.000 7.490 0.000 0.000 0.000 4.970 0.000 d4 0 . 0 0 1 0.000 10.270 0.000 0 . 0 0 0 0.000 7.250 0.000 d5 0 . 0 0 1 0.000 14.000 0.000 0 . 0 0 1 0.000 11.420 0.000 d6 0 . 0 0 1 0.000 12.850 0.000 0 . 0 0 1 0.000 1 0 . 0 1 0 0.000 d7 0 . 0 0 1 0.000 13.920 0.000 0 . 0 0 1 0.000 1 1 . 2 1 0 0.000 d8 0.000 0.000 11.640 0.000 0.000 0.000 8.970 0.000 d9 0 . 0 0 1 0.000 18.760 0.000 0 . 0 0 1 0.000 12.770 0.000 d1 0 0 . 0 0 1 0.000 17.760 0.000 0 . 0 0 1 0.000 13.580 0.000 d1 1 0.000 0.000 11.400 0.000 0.000 0.000 7.930 0.000
employment share in state industry (dropped) 0 . 0 0 1 0.000 2.830 0.005
employment share in agriculture (dropped) 0.000 0.000 -2.990 0.003
_cons 0 . 0 0 2 0 . 0 0 1 3.090 0 . 0 0 2 -0 . 0 0 1 0.000 -3.570 0.000
Number of observations 743 743
Number of groups 2 1 2 1
Min Observations per group 1 0 1 0
Avg Observations per group 35.4 35.4
Max Observations per group 53 53
R square within 0.706 0.663
R square between 0.742 0 . 8 6 6
R square overall 0.634 0.771
Hausman Test 0 0
Table 30 Cumulative significance of coefficients for old agricultural voivodeships.
Itype of ALMPs joint effects for old agricultural voivodeships
fixed effects random effects
llntervention Works |F( 7, 690)= 1.47 Prob > F = 0.1746 Ichi2( 7)= 7.06 Prob > chi2 = 0.4223 Public Works F( 5, 690)= 4.13 Prob > F = 0.0010 chi2( 5)= 14.44 Prob > chi2 = 0.0130 Loans F( 6, 690) = 2.50 Prob > F = 0.0214 ch!2( 6)= 1 2 . 0 1 Prob > chi2 = 0.0616
In the case o f the industrial voivodeships, the results indicate the m edium -range efficiency o f A L M P o f all the groups. T he results for this group can be found in Table 31. T he results have the sam e pattern as general results with a positive effect in case of training (cum ulative coefficient o f 0.011) and positive external effect o f public works (cum ulative coefficient o f 0.02) and a negative external effect in case o f intervention works (cum ulative coefficient o f -0.107). Start up loans have a cum ulative coefficient o f -0.051, indicating substitution/ displacem ent effects.
Table 31 Augmented matching function estimates for old industrial voivodeships.
Fixed Effects Random Effects
Outflows Coefficient Std. Error T-statistics P*|t| Coefficient Std. Error Z-statistics Z>|t|
In unemployment (t-1) 0.380 0.049 7.750 0.000 0.576 0.035 16.420 0.000 In vacancies (t-1) 0.070 0.017 4.190 0.000 0.054 0 . 0 1 0 5.140 0.000 Training 0 . 0 1 1 0.013 0.860 0.391 0.063 0 . 0 1 1 5.950 0.000 In Intervention Works 0.045 0.026 1.730 0.085 0.036 0.028 1.300 0.193 In Intervention Works (t-1) -0.084 0.026 -3.190 0 . 0 0 2 -0.079 0.029 -2.680 0.007 In Intervention Works (t-2) -0 . 0 1 2 0.023 -0.530 0.595 -0.006 0.029 -0.190 0.846 In Intervention Works (t-3) -0.016 0 . 0 2 2 -0.730 0.467 -0 . 0 1 0 0.025 -0.380 0.707 In Intervention Works (t-4) -0 . 0 1 2 0.019 -0.660 0.511 -0.003 0.024 -0 . 1 2 0 0.905 In Intervention Works (t-5) -0 . 0 1 1 0 . 0 2 0 -0.530 0.598 -0.005 0.024 -0 . 2 0 0 0.845 In Intervention Works (t-6) -0.016 0.019 -0.880 0.378 0.013 0 . 0 2 2 0.610 0.542 In Public Works 0.023 0.015 1.550 0.123 0.016 0.014 1.150 0.249 In Public Works (t-1) 0 . 0 1 0 0 . 0 1 2 0.880 0.379 0.017 0.014 1.250 0 . 2 1 2 In Public Works (t-2) -0.006 0 . 0 1 2 -0.540 0.590 -0.005 0.014 -0.350 0.724 In Public Works (t-3) 0.009 0 . 0 1 2 0.740 0.460 0 . 0 1 0 0.014 0.690 0.491 In Public Works (t-4) -0.015 0 . 0 1 1 -1.370 0.170 -0.017 0 . 0 1 2 -1.420 0.157 in Startup Loans 0.003 0.014 0 . 2 1 0 0.833 0 . 0 0 1 0.014 0.070 0.945 In Startup Loans (t-1) 0.003 0.013 0.230 0.818 0.000 0.015 -0 . 0 2 0 0.983 In Startup Loans (t-2) 0 . 0 1 1 0 . 0 1 2 0.940 0.348 0 . 0 1 0 0.014 0.710 0.480 In Startup Loans (t-3) -0.014 0 . 0 1 1 -1.290 0.196 -0 . 0 2 0 0.013 -1.570 0.117 In Startup Loans (t-4) -0.016 0 . 0 1 2 -1.360 0.173 -0.019 0.013 -1.470 0.142 In Startup Loans (t-5) -0.037 0 . 0 1 0 -3.640 0.000 -0.046 0 . 0 1 1 -4.140 0.000 d1 0.000 0.000 3.930 0.000 0.000 0.000 4.020 0.000 d2 0.000 0.000 4.490 0.000 0.000 0.000 3.960 0.000 d3 0.000 0.000 8.160 0.000 0.000 0.000 6.780 0.000 d4 0.000 0.000 8.340 0.000 0.000 0.000 7.130 0.000 d5 0 . 0 0 1 0.000 12.480 0.000 0.000 0.000 10.810 0.000 d6 0.000 0.000 9.180 0.000 0.000 0.000 8.300 0.000 d7 0.000 0.000 9.920 0.000 0.000 0.000 9.320 0.000 d8 0.000 0.000 8.840 0.000 0.000 0.000 8 . 2 2 0 0.000 d9 0 . 0 0 1 0.000 15.180 0.000 0 . 0 0 1 0.000 14.170 0.000 dIO 0 . 0 0 1 0.000 15.440 0.000 0 . 0 0 1 0.000 13.520 0.000 d11 0.000 0.000 9.940 0.000 0.000 0.000 8.710 0.000
employment share in state industry (dropped) 0 . 0 0 1 0.000 6.830 0.000
employment share in agriculture (dropped) 0 . 0 0 1 0.000 8.670 0.000
_cons 0.003 0 . 0 0 1 6 . 1 0 0 0.000 0.000 0.000 0.510 0.612
Number of observations 692 692
Number of groups 16 16
Min Observations per group 28 28
Avg Observations per group 43.3 43.3
Max Observations per group 54 54
R square within 0 . 6 8 6 0.664
R square between 0.842 0.944
R square overall 0.695 0.827
Hausman Test 0 0
Table 32 Cumulative significance of coefficients for old industrial voivodeships.
type of ALMPs joint effects for old industrial voivodeships
fixed effects random effects
Intervention Works F( 7, 644) = 4.70 Prob > F = 0.0000 chi2( 7)= 16.27 Prob > chi2 s 0.0227 Public Works F( 5, 644) = 1.94 Prob > F = 0.0854 chi2( 5)= 10.87 Prob > chi2 3 0.0540 Loans F( 6, 644) = 3.92 Prob > F = 0.0007 chi2( 6)= 44.02 Prob > chi2 3 0.0000
T he results for m odem voivodeships region can be found in Table 33. T he estim ation results for m odern voivodeships show the highest efficiency betw een all types for training with a coefficient o f 0.024. The highest efficiency is also dem onstrated by the estim ation results on public w orks with the cum ulative coefficient o f 0.097, show ing positive external effects. Intervention w orks are the least efficient in the m odem voivodeships with a cum ulative coefficient o f -0.14, indicating substitution/ displacem ent effects. Start up loans have the highest efficiency, but still show substitution/ displacem ent effects w ith the cum ulative coefficient o f -0.002.
Table 33 Augmented matching function estimates for old modern voivodeships.
Fixed Effects Random Effects
Outflows Coefficient Std. Error T-statistiCS P>|t| Coefficient Std. Error Z-statistics Z>|t|
In unemployment (t-1) 0.500 0.047 10.590 0.000 0.514 0.040 12.980 0.000 In vacancies (t-1) 0.086 0.027 3.220 0.001 0.089 0.013 6.640 0.000 Training 0.024 0.019 1.260 0.207 0.030 0.015 1.910 0.057 In Intervention Works -0.014 0.042 -0.340 0.734 0.027 0.038 0.720 0.473 In Intervention Works (t-1) -0.062 0.043 -1.440 0.152 -0.056 0.044 -1.250 0.210 In Intervention Works (t-2) -0.028 0.034 -0.830 0.409 -0.028 0.036 -0.800 0.425 In Intervention Works (t-3) 0.027 0.035 0.780 0.437 0.042 0.037 1.120 0.263 In Intervention Works (t-4) -0.037 0.036 -1.030 0.304 -0.025 0.038 -0.650 0.513 In Intervention Works (t-5) -0.014 0.035 -0.390 0.695 -0.004 0.036 -0.120 0.907 In Intervention Works (t-6) -0.012 0.030 -0.410 0.684 0.011 0.030 0.370 0.710 In Public Works 0.047 0.019 2.530 0.012 0.040 0.017 2.280 0.022 In Public Works (t-1) -0.008 0.014 -0.540 0.589 -0.011 0.016 -0.660 0.507 In Public Works (t-2) 0.004 0.016 0.270 0.787 0.002 0.016 0.130 0.896 In Public Works (t-3) 0.034 0.016 2.100 0.037 0.026 0.017 1.510 0.132 In Public Works (t-4) 0.019 0.017 1.120 0.264 0.007 0.016 0.400 0.688 In Startup Loans -0.006 0.018 -0.320 0.746 0.010 0.018 0.550 0.581 In Startup Loans (t-1) 0.013 0.015 0.820 0.414 0.021 0.016 1.350 0.176 In Startup Loans (t-2) -0.012 0.015 -0.860 0.392 -0.007 0.015 -0.490 0.625 In Startup Loans (t-3) -0.002 0.014 -0.120 0.904 0.002 0.016 0.120 0.903 In Startup Loans (t-4) 0.005 0.016 0.320 0.752 0.011 0.018 0.580 0.564 In Startup Loans (t-5) 0.000 0.014 0.010 0.990 0.012 0.015 0.790 0.428 dl 0.000 0.000 2.210 0.028 0.000 0.000 2.670 0.008 d2 0.000 0.000 4.700 0.000 0.000 0.000 4.570 0.000 d3 0.000 0.000 6.540 0.000 0.000 0.000 5.980 0.000 d4 0.000 0.000 6.300 0.000 0.000 0.000 5.940 0.000 d5 0.001 0.000 11.250 0.000 0.001 0.000 9.960 0.000 d6 0.001 0.000 9.800 0.000 0.001 0.000 9.690 0.000 d7 0.000 0.000 7.650 0.000 0.000 0.000 7.590 0.000 d8 0.000 0.000 3.830 0.000 0.000 0.000 3.900 0.000 d9 0.001 0.000 8.060 0.000 0.001 0.000 8.110 0.000 d10 0.001 0.000 8.240 0.000 0.000 0.000 7.970 0.000 d11 0.000 0.000 6.420 0.000 0.000 0.000 5.990 0.000
employment share in state industry (dropped) -0.001 0.001 -1.420 0.155
employment share in agriculture (dropped) 0.000 0.000 1.920 0.055
_cons 0.001 0.000 2.960 0.003 0.001 0.000 1.900 0.057
Number of observations 358 358
Number of groups 8 8
Min Observations per group 29 29
Avg Observations per group 44.8 44.8
Max Observations per group 54 54
R square within 0.741 0.729
R square between 0.573 0.923
R square overall 0.695 0.769
Hausman Test 0.9998 0.9998
Table 34 Cumulative significance of coefficients for old modern voivodeships.
type of ALMP joint effects for old modern voivodeships
fixed effects random effects
Intervention Works F( 7, 318)= 2.13 Prob > F = 0.0401 chi2( 7) = 5.29 Prob > chi2 = 0.6241 Public Works F( 5, 318)= 2.18 Prob > F = 0.0561 chi2( 5)= 8.63 Prob > chi2 = 0.1246 Loans F( 6, 318) = 0.20 Prob > F = 0.9755 Chl2( 6)= 7.76 Prob > chi2 = 0.2566
C om parison o f A LM Ps results for the three types o f voivodeships provides a m ixed picture. Clearly, A L M P seem s to be m ost efficient in the m odem voivodeships and least efficient in local econom ies characterised by ‘old’ types o f econom ic structures. T his would indicate that som e policies supporting structural adjustm ent like incentives for investors, or infrastructure upgrade, m ay be m ore effective than A L M P to com bat unem ploym ent in the regions that are econom ically “backw ard” .
6.4 Conclusions
Overall, the results dem onstrate som e o f the A LM Ps as having a negative substitution/ displacem ent effects. In particular that relates to intervention works, w hich is a very crude policy measure. On the other hand, m ore sophisticated policies, especially the training program m es seem to be efficient. M oreover, there is a positive change in the results o f the training program m es when com pared with the results for the earlier and for the more recent period. H ow ever, another policy, which has also a clear-cut positive external effect, is public works.
The results obtained for new voivodeships, with the exclusion o f the recession period o f 2001-2002 m atch the results for old voivodeships. The coefficients for public w orks and intervention w orks are alm ost the same, and the effect o f training has a stronger positive effect in the case o f new voivodeships, show ing the im proved efficiency of this policy. These tests, lead to an im portant conclusion that the im pact o f the A L M Ps
is conditional on the overall m acroeconom ic conditions, being w eaker when the overall level o f econom ic activity is w eaker. The policy im plication o f this finding is that while som e types o f A L M Ps (see above) m ay be efficient w ith dealing with specific social unem ploym ent related problem s, the A LM P cannot be used as an effective countercyclical m acro policy tool, or any sort o f autom atic m acro stabiliser.
A nother im portant result is obtained when I analyse groups o f voivodeships, divided into agricultural, m odem and industrial voivodeships. I dem ostrate that the differences betw een regions lead to different patterns o f policy im pact. The type o f regional structure which is m ost different to the others is the agricultural type, w here also the unem ploym ent level is the highest. The effects o f training, unlike in the other types (in the earlier period, i.e. in the old voivodeships) are negative, w hile the effect o f intervention works is positive. The policies such as training are the m ost efficient in the m odern voivodeships, w here the efficiency o f intervention w orks is the lowest. This strongly suggests that the policy m easures should be calibrated according to the type o f the regional econom ic structures and that the m ore sophisticated types o f ALM P are not very effective in the m ore traditional structural environm ent. In contrast, even if perheps not surprisingly, som e o f the m ore crude policy m easures (especially: intervention works) perform better there. In short, m ore sophisticated policies are m ore appropriate to m ore sophisticated sectoral structures.
These findings are in line with the results o f VAR analysis o f regional labour m arket dynam ics, which show ed a great persistence in unem ploym ent, especially in the poorer regions, indicating that these regions do not respond in the sam e way as the other regions do. I now offer a novel explanation to this structural outcom e: the existing persistence in inter-regional differences in unem ploym ent m ay be (partly)
resulting from the fact that the policy m easures are not adjusted and are not used selectively consistent w ith the local econom ic environm ent.
The results perm it the draw ing out o f a tentative conclusion that A L M Ps in Poland should be reassessed as for their efficiency, as m any o f the m easures used m ight constitute a loss o f tax payers m oney, substitution or displacem ent effect. Also, these policies should be carefully designed and should take into account different types o f regions, which m ight have different needs and requirem ents.
Chapter 7 Conclusions
In the thesis I have aim ed to analyse the regional labour m arket dynam ics and effectiveness o f active labour m arket policies in alleviating the regional differences in unem ploym ent. M y m ain research focus was to assess the effectiveness o f the elem ents of A LM Ps and to suggest possible explanations o f the heterogeneity in their outcom es. These explanations focused on the process o f econom ic transition (liberalisation) highlighting its im pact on restructuring and on labour m arkets, especially on their spatial dim ension. My objective was to com bine the existing labour m arket- and transition theory with new em pirical research and to arrive at recom m endations regarding the appropriate m ix o f policy response to the unem ploym ent problem . In addition, I analysed the experiences o f the Spanish labour m arket reforms to understand the likely trajectory induced by a sim ilar liberalisation process and labour m arket institutional reform in Poland. That enabled m e to see the post-com m unist transition process in the com parative context o f the post-fascist liberalisation program m e. A related policy objective was to explore if and w hich o f the institutional reform s in Spain led to better labour m arket perform ance and if the same outcom e m ay be expected in Poland if sim ilar reform s are im plem ented.
This research program m e was successfully im plem ented. Through the em pirical (econom etric) part o f thesis, where the persistence o f unem ploym ent in the Polish and Spanish labour m arkets was investigated, I dem onstrated that the unem ploym ent problem cannot be understood just by looking at the aggregate figures. T his is because the results indicate very strong regional unem ploym ent persistence, especially in poorer regions. I also m easured the effectiveness o f the labour m arket policies and
com bining these results with the theoretical perspective on restructuring in transition, I drew conclusions for the policy m akers, arguing that the unem ploym ent problem can be alleviated if the appropriate policy mix is applied.
In my em pirical design, I focused on the Polish econom y. The Polish labour m arket has been subject to a very powerful transform ation since 1989. T he introduction o f m arket principles apart from the positive results was accom panied by side effects, m ost significantly unem ploym ent. I argued that the rise in unem ploym ent in Poland was prim arily an effect o f jo b destruction through deindustrialisation and the restructuring process, w hich resulted from the political and econom ic transition. T he m ain challenge for the econom y as a whole was to m atch this with jo b creation in the new sector, which was in turn dependent on a high rate o f grow th in production and a high rate o f labour productivity growth. The econom y faced and still faces restructuring challenges o f specific industrial branches including coal m ining, m etallurgy, energy production, the arms industry, the shipbuilding industry, rail transport and agriculture. In this thesis I have sought to dem onstrate that this structural dim ension cannot be neglected, as it translates into the heterogeneity o f regional outcom es. H ow ever, I argue that while the adequately addressed labour m arket policy tools m ay reduce inter-regional differences, the overall success o f labour m arket policy may be conditional on im plem enting the labour m arket reform s sim ilar to those recently im plem ented in Spain. In particular, lifting o f some institutional barriers like the lim itations in the labour code should be reconsidered.
M oreover, som e policies intended to alleviate the problem o f unem ploym ent, do not necessarily produce the expected results, either because they have undesired indirect effects (that includes relatively easy access to early retirem ent, and other social
benefits) or seem to be inefficient or not calibrated according to the local needs (that relates to some elem ents o f A L M P s as docum ented by m y em pirical results).
Overall, im proving the Polish labour m arket is an enorm ous challenge for the country’s governm ent. H ow ever, over the past 30 years, there were a num ber o f O E C D countries (Spain, Luxem bourg, the N etherlands, N orw ay, the U nited States) that have succeeded in raising overall em ploym ent rates by 10 or m ore percentage points. The nature o f those countries’ problem s was different in each case. N evertheless, the im provem ents were the results o f a fundam ental labour m arket reform.
A s discussed, the country whose situation was the m ost com parable w ith that of Poland is Spain. Even though, the level o f developm ent and the international m acroeconom ic scenarios were different when Spain entered the EU in 1986, to those encountered alm ost 20 years after, when Poland and other 9 countries jo in ed the EU in 2004, the transform ations o f both countries required substantial labour reallocation from resource-intensive to labour intensive activities, and from agriculture and m anufacturing to services. Therefore, it is argued that the analysis o f the Spanish experiences could be o f benefit in the Polish case and m ore generally, that the focus on the restructuring process is essential for any successful theoretical analysis o f the corresponding labour m arket outcom es.
Based on the Spanish experience it can be concluded that too rigid labour legislation im pedes em ploym ent grow th or even contributes to em ploym ent destruction. A rigid institutional fram ew ork m ay convert tem porary shocks into perm anent ones creating a hysteresis effect, and this is the institutional context o f m y results on regional persistence in unem ploym ent rates in both countries. A flexible labour m arket allow s
for a better accom m odating o f the labour reallocation. The Spanish case show s that restrictive em ploym ent protection legislation in the tim es o f deindustrialisation and shifts in the sectoral com position o f em ploym ent was one o f the reasons for rising unem ploym ent levels. In addition, generous and long-lasting unem ploym ent benefits were also contributing to hysteresis and unem ploym ent persistence.
Through the analysis o f the developm ents o f regional labour m arkets in Poland and Spain since their transition to dem ocracy and the open m arket I have highlighted the sim ilarities betw een these tw o econom ies. By investigating the extent to w hich labour m arket shocks are shared by all regions and how regional em ploym ent, unem ploym ent and labour force participation adjust to labour dem and shocks w hich are region- specific, I have show n that on average 32% o f the changes in regional em ploym ent are shared by all regions in Spain and 17% in Poland. In term s o f participation rate, on average 65% o f the changes in regional participation rate are shared by all regions in Spain and 46% in Poland.